Jenni Raitoharju

Jenni Raitoharju is an Assistant Professor at the University of Jyväskylä's Faculty of Information Technology. Her research focuses on developing novel machine learning algorithms with applications in environmental studies, autonomous systems, and computer vision. Key areas include deep learning, uncertainty estimation, one-class classification, and statistical machine learning. She leads projects such as the 2024–2026 Ministry of Environment-funded study on aquatic biodiversity monitoring and a 2023 Finnish National Agency for Education project on morphotaxonomic classification.

Her work bridges theoretical advancements with practical implementations, as seen in publications like Linear-Time One-Class Classification with Repeated Element-Wise Folding (2024) and AquaMonitor: A multimodal multi-view image sequence dataset (2025). She actively contributes to maritime computer vision initiatives, including organizing the 2023 MaCVi Workshop. Jenni's research also involves ecological instrumentation, such as the Riverine Organism Drift Imager (RODI) for aquatic organism studies.

Her projects emphasize interdisciplinary collaboration, combining machine learning with environmental science and engineering. While no awards or grants are explicitly listed, her 17+ publications since 2011 reflect sustained academic productivity. She coordinates lab efforts focusing on AI-driven ecological monitoring and autonomous systems.